{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kR-4eNdK6lYS"
   },
   "source": [
    "<h1 align=\"center\"> MNIST TensorFlow </h1>\n",
    "\n",
    "A large part of this tutorial's purpose is to visualize weights using TensorFlow\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "cellView": "both",
    "colab": {
     "autoexec": {
      "startup": false,
      "wait_interval": 0
     }
    },
    "colab_type": "code",
    "collapsed": true,
    "id": "JLpLa8Jt7Vu4"
   },
   "outputs": [],
   "source": [
    "# These are all the modules we'll be using later. Make sure you can import them\n",
    "# before proceeding further.\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from struct import unpack\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look into normalizing the data here. Maybe not instructional to have separate cell on it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def loadmnist(imagefile, labelfile):\n",
    "\n",
    "    # Open the images with gzip in read binary mode\n",
    "    images = open(imagefile, 'rb')\n",
    "    labels = open(labelfile, 'rb')\n",
    "\n",
    "    # Get metadata for images\n",
    "    images.read(4)  # skip the magic_number\n",
    "    number_of_images = images.read(4)\n",
    "    number_of_images = unpack('>I', number_of_images)[0]\n",
    "    rows = images.read(4)\n",
    "    rows = unpack('>I', rows)[0]\n",
    "    cols = images.read(4)\n",
    "    cols = unpack('>I', cols)[0]\n",
    "\n",
    "    # Get metadata for labels\n",
    "    labels.read(4)\n",
    "    N = labels.read(4)\n",
    "    N = unpack('>I', N)[0]\n",
    "\n",
    "    # Get data\n",
    "    x = np.zeros((N, rows*cols), dtype=np.float32)  # Initialize numpy array\n",
    "    \n",
    "    # 10 because there are 10 classes; the numbers 1-10\n",
    "    y = np.zeros(N, dtype=np.float32)  # Initialize numpy array\n",
    "    for i in range(N):\n",
    "        for j in range(rows*cols):\n",
    "            tmp_pixel = images.read(1)  # Just a single byte\n",
    "            tmp_pixel = unpack('>B', tmp_pixel)[0]\n",
    "            x[i][j] = tmp_pixel\n",
    "        tmp_label = labels.read(1)\n",
    "        y[i] = unpack('>B', tmp_label)[0]\n",
    "\n",
    "    y = (np.arange(10) == y[:,None]).astype(np.float32)\n",
    "    images.close()\n",
    "    labels.close()\n",
    "    return (x, y)\n",
    "\n",
    "def non_flattened_image(image):\n",
    "    plt.imshow(np.reshape(image, (28,28)), cmap=plt.cm.gray)\n",
    "    plt.axis('off')\n",
    "    plt.show()\n",
    "\n",
    "def flattened_image(image):\n",
    "    plt.imshow(np.reshape(image, (784,1)), cmap=plt.cm.gray)\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "train_img_path = 'train-images-idx3-ubyte' \n",
    "train_lbl_path = 'train-labels-idx1-ubyte' \n",
    "test_img_path = 't10k-images-idx3-ubyte' \n",
    "test_lbl_path = 't10k-labels-idx1-ubyte'\n",
    "\n",
    "x_train, y_train = loadmnist(train_img_path, train_lbl_path)\n",
    "#x_train, y_train = np.array(x_train).real.astype(np.float32, copy=False), np.array(y_train).real.astype(np.float32, copy=False) \n",
    "testing_data, testing_labels = loadmnist(test_img_path, test_lbl_path)\n",
    "#testing_data, testing_labels = np.array(testing_data).real.astype(np.float32, copy=False), np.array(testing_labels).real.astype(np.float32, copy=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 10)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.], dtype=float32)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"left\"> Making a Validation Test Set </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "training, validation, and test data set explanation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60000, 784)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The shape of the dataset is 60000 by 784. Each of the images is shaped flattened\n",
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training size: 45000\n",
      "validation size: 15000\n",
      "training + validation size: 60000\n"
     ]
    }
   ],
   "source": [
    "training_number = len(x_train) * 3/4\n",
    "validation_number = len(x_train) * 1/4\n",
    "total_number = training_number + validation_number\n",
    "print ('training size: {}'.format(training_number))\n",
    "print ('validation size: {}'.format(validation_number))\n",
    "print ('training + validation size: {}'.format(training_number + validation_number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_data, validation_data = x_train[0:training_number], x_train[training_number:total_number]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "training_labels, validation_labels = y_train[0:training_number], y_train[training_number:total_number]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "<h1 align=\"left\"> Show example of the data </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"left\"> How the Computer Sees the Data With Multinomial Logistic Regression </h3>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is Logistic Regression? What is Multinomial Logistic Regression (Softmax Regression)?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This model doesn't account for the interrelationships between pixels as you can see below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAADQAAAEDCAYAAAB6cCdKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQnQkREdCdCRER0J0JERHQnQkREdCdP4CWQFV4beoe8AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11f05dd10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "flattened_image(training_data[0,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3 align=\"left\"> How we See the Data </h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQEAAAD/CAYAAADxA2MgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ndAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x141397b50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_flattened_image(training_data[0,:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"left\"> What is Logistic Regression? </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "research from tensorflow website"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"left\"> What is Multinomial Logistic Regression (Softmax Regression)? </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "research from tensorflow website (maybe the image from the website) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're first going to train a multinomial logistic regression (sigmoid regression) using simple gradient descent.\n",
    "\n",
    "TensorFlow works like this:\n",
    "* First you describe the computation that you want to see performed: what the inputs, the variables, and the operations look like. These get created as nodes over a computation graph. This description is all contained within the block below:\n",
    "\n",
    "      with graph.as_default():\n",
    "          ...\n",
    "\n",
    "* Then you can run the operations on this graph as many times as you want by calling `session.run()`, providing it outputs to fetch from the graph that get returned. This runtime operation is all contained in the block below:\n",
    "\n",
    "      with tf.Session(graph=graph) as session:\n",
    "          ...\n",
    "\n",
    "Let's load all the data into TensorFlow and build the computation graph corresponding to our training:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# With gradient descent training, even this much data is prohibitive.\n",
    "# Subset the training data for faster turnaround.\n",
    "train_subset = 10000\n",
    "image_size = 28\n",
    "num_labels = 10\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "\n",
    "    # Input data.\n",
    "    # Load the training, validation and test data into constants that are\n",
    "    # attached to the graph.\n",
    "    tf_train_dataset = tf.constant(training_data[:train_subset, :])\n",
    "    tf_train_labels = tf.constant(training_labels[:train_subset])\n",
    "    tf_valid_dataset = tf.constant(validation_data)\n",
    "    tf_test_dataset = tf.constant(testing_data)\n",
    "  \n",
    "    # Variables.\n",
    "    # These are the parameters that we are going to be training. The weight\n",
    "    # matrix will be initialized using random values following a (truncated)\n",
    "    # normal distribution. The biases get initialized to zero.\n",
    "    weights = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, num_labels]))\n",
    "    biases = tf.Variable(tf.zeros([num_labels]))\n",
    "  \n",
    "    # Training computation.\n",
    "    # We multiply the inputs with the weight matrix, and add biases. We compute\n",
    "    # the softmax and cross-entropy (it's one operation in TensorFlow, because\n",
    "    # it's very common, and it can be optimized). We take the average of this\n",
    "    # cross-entropy across all training examples: that's our loss.\n",
    "    logits = tf.matmul(tf_train_dataset, weights) + biases\n",
    "    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits, tf_train_labels))\n",
    "  \n",
    "\n",
    "    ## this next commented line is equalivalent to logits\n",
    "    # tf.matmul(tf_train_dataset, weights) + biases  \n",
    "    #pred = tf.nn.softmax(tf.matmul(tf_train_dataset, weights) + biases) # Softmax\n",
    "\n",
    "    # Minimize error using cross entropy\n",
    "    #cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))\n",
    "    # Gradient Descent\n",
    "    #optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)\n",
    "    ### Testing \n",
    "    \n",
    "    \n",
    "    # Optimizer.\n",
    "    # We are going to find the minimum of this loss using gradient descent.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
    "  \n",
    "    # Predictions for the training, validation, and test data.\n",
    "    # These are not part of training, but merely here so that we can report\n",
    "    # accuracy figures as we train.\n",
    "    train_prediction = tf.nn.softmax(logits)\n",
    "    valid_prediction = tf.nn.softmax(tf.matmul(tf_valid_dataset, weights) + biases)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf_test_dataset, weights) + biases)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Let's run this computation and iterate:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "V_accur_list = [] #verification accuracy list\n",
    "step_list = [] # step number list\n",
    "cost_list = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Loss at step 0: 3355.098389\n",
      "Training accuracy: 8.0%\n",
      "Validation accuracy: 38.6%\n",
      "Loss at step 20: 796.188354\n",
      "Training accuracy: 81.7%\n",
      "Validation accuracy: 82.1%\n",
      "Loss at step 40: 337.566193\n",
      "Training accuracy: 89.9%\n",
      "Validation accuracy: 88.1%\n",
      "Loss at step 60: 1818.627808\n",
      "Training accuracy: 70.0%\n",
      "Validation accuracy: 74.3%\n",
      "Loss at step 80: 257.265137\n",
      "Training accuracy: 91.0%\n",
      "Validation accuracy: 88.7%\n",
      "Loss at step 100: 364.349884\n",
      "Training accuracy: 88.5%\n",
      "Validation accuracy: 85.6%\n",
      "Loss at step 120: 230.650208\n",
      "Training accuracy: 91.9%\n",
      "Validation accuracy: 89.2%\n",
      "Loss at step 140: 340.510437\n",
      "Training accuracy: 86.9%\n",
      "Validation accuracy: 78.9%\n",
      "Loss at step 160: 206.061447\n",
      "Training accuracy: 92.4%\n",
      "Validation accuracy: 89.5%\n",
      "Loss at step 180: 896.965576\n",
      "Training accuracy: 83.7%\n",
      "Validation accuracy: 80.3%\n",
      "Loss at step 200: 302.299438\n",
      "Training accuracy: 90.1%\n",
      "Validation accuracy: 89.1%\n",
      "Loss at step 220: 208.521164\n",
      "Training accuracy: 91.0%\n",
      "Validation accuracy: 87.5%\n",
      "Loss at step 240: 169.962143\n",
      "Training accuracy: 93.0%\n",
      "Validation accuracy: 89.7%\n",
      "Loss at step 260: 223.820801\n",
      "Training accuracy: 89.7%\n",
      "Validation accuracy: 84.9%\n",
      "Loss at step 280: 157.640121\n",
      "Training accuracy: 92.8%\n",
      "Validation accuracy: 89.5%\n",
      "Loss at step 300: 489.743896\n",
      "Training accuracy: 81.8%\n",
      "Validation accuracy: 77.4%\n",
      "Loss at step 320: 139.685486\n",
      "Training accuracy: 93.3%\n",
      "Validation accuracy: 89.7%\n",
      "Loss at step 340: 777.528625\n",
      "Training accuracy: 81.1%\n",
      "Validation accuracy: 81.5%\n",
      "Loss at step 360: 145.049347\n",
      "Training accuracy: 93.0%\n",
      "Validation accuracy: 89.3%\n",
      "Loss at step 380: 160.409088\n",
      "Training accuracy: 92.9%\n",
      "Validation accuracy: 89.4%\n",
      "Loss at step 400: 2253.112305\n",
      "Training accuracy: 79.0%\n",
      "Validation accuracy: 80.3%\n",
      "Loss at step 420: 131.940308\n",
      "Training accuracy: 92.9%\n",
      "Validation accuracy: 88.7%\n",
      "Loss at step 440: 153.688950\n",
      "Training accuracy: 93.3%\n",
      "Validation accuracy: 89.7%\n",
      "Loss at step 460: 163.620880\n",
      "Training accuracy: 91.5%\n",
      "Validation accuracy: 87.3%\n",
      "Loss at step 480: 137.221359\n",
      "Training accuracy: 93.7%\n",
      "Validation accuracy: 89.9%\n",
      "Loss at step 500: 179.724625\n",
      "Training accuracy: 90.7%\n",
      "Validation accuracy: 86.0%\n",
      "Loss at step 520: 112.583534\n",
      "Training accuracy: 94.0%\n",
      "Validation accuracy: 89.5%\n",
      "Loss at step 540: 697.804077\n",
      "Training accuracy: 82.4%\n",
      "Validation accuracy: 70.4%\n",
      "Loss at step 560: 111.165451\n",
      "Training accuracy: 93.7%\n",
      "Validation accuracy: 89.0%\n",
      "Loss at step 580: 329.445923\n",
      "Training accuracy: 89.3%\n",
      "Validation accuracy: 88.6%\n",
      "Loss at step 600: 126.771576\n",
      "Training accuracy: 92.9%\n",
      "Validation accuracy: 88.5%\n",
      "Loss at step 620: 107.159912\n",
      "Training accuracy: 93.9%\n",
      "Validation accuracy: 89.2%\n",
      "Loss at step 640: 184.384232\n",
      "Training accuracy: 92.1%\n",
      "Validation accuracy: 89.6%\n",
      "Loss at step 660: 109.565002\n",
      "Training accuracy: 94.0%\n",
      "Validation accuracy: 89.3%\n",
      "Loss at step 680: 829.370728\n",
      "Training accuracy: 78.6%\n",
      "Validation accuracy: 74.0%\n",
      "Loss at step 700: 109.563301\n",
      "Training accuracy: 94.2%\n",
      "Validation accuracy: 89.3%\n",
      "Loss at step 720: 111.219299\n",
      "Training accuracy: 93.5%\n",
      "Validation accuracy: 88.6%\n",
      "Loss at step 740: 687.414673\n",
      "Training accuracy: 80.9%\n",
      "Validation accuracy: 75.2%\n",
      "Loss at step 760: 103.966515\n",
      "Training accuracy: 94.2%\n",
      "Validation accuracy: 89.2%\n",
      "Loss at step 780: 365.990173\n",
      "Training accuracy: 85.0%\n",
      "Validation accuracy: 80.4%\n",
      "Loss at step 800: 94.056885\n",
      "Training accuracy: 94.9%\n",
      "Validation accuracy: 89.4%\n",
      "Loss at step 820: 106.879150\n",
      "Training accuracy: 93.7%\n",
      "Validation accuracy: 88.3%\n",
      "Loss at step 840: 2014.399414\n",
      "Training accuracy: 73.9%\n",
      "Validation accuracy: 78.3%\n",
      "Loss at step 860: 93.211746\n",
      "Training accuracy: 94.6%\n",
      "Validation accuracy: 89.3%\n",
      "Loss at step 880: 997.719421\n",
      "Training accuracy: 79.5%\n",
      "Validation accuracy: 73.5%\n",
      "Loss at step 900: 99.152138\n",
      "Training accuracy: 94.0%\n",
      "Validation accuracy: 88.8%\n",
      "Loss at step 920: 480.087311\n",
      "Training accuracy: 83.6%\n",
      "Validation accuracy: 75.0%\n",
      "Loss at step 940: 85.573845\n",
      "Training accuracy: 94.8%\n",
      "Validation accuracy: 89.0%\n",
      "Loss at step 960: 240.113220\n",
      "Training accuracy: 87.7%\n",
      "Validation accuracy: 81.0%\n",
      "Loss at step 980: 108.715523\n",
      "Training accuracy: 93.7%\n",
      "Validation accuracy: 88.7%\n",
      "Test accuracy: 88.4%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1000\n",
    "\n",
    "def accuracy(predictions, labels):\n",
    "    return (100.0 * np.sum(np.argmax(predictions, 1) == np.argmax(labels, 1)) / predictions.shape[0])\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    # This is a one-time operation which ensures the parameters get initialized as\n",
    "    # we described in the graph: random weights for the matrix, zeros for the\n",
    "    # biases. \n",
    "    tf.initialize_all_variables().run()\n",
    "    print('Initialized')\n",
    "    for step in range(num_steps):\n",
    "        # Run the computations. We tell .run() that we want to run the optimizer,\n",
    "        # and get the loss value and the training predictions returned as numpy\n",
    "        # arrays.\n",
    "        _, l, predictions = session.run([optimizer, loss, train_prediction])\n",
    "        if (step % 20 == 0):\n",
    "            cost_list.append(l)\n",
    "            step_list.append(step)\n",
    "            print('Loss at step %d: %f' % (step, l))\n",
    "            print('Training accuracy: %.1f%%' % accuracy(\n",
    "            predictions, y_train[:train_subset, :]))\n",
    "            # Calling .eval() on valid_prediction is basically like calling run(), but\n",
    "            # just to get that one numpy array. Note that it recomputes all its graph\n",
    "            # dependencies.\n",
    "            valid_accuracy = accuracy(valid_prediction.eval(), validation_labels)\n",
    "            V_accur_list.append(valid_accuracy)\n",
    "            print('Validation accuracy: %.1f%%' % valid_accuracy)\n",
    "    test_accuracy = accuracy(test_prediction.eval(), testing_labels)\n",
    "    print('Test accuracy: %.1f%%' % test_accuracy)\n",
    "    #This line below allows for taking the predictions to a pandas dataframe and eventually a heatmap/confusion matrix\n",
    "    pred = test_prediction.eval()\n",
    "    weights_matrix = weights.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABsoAAAJqCAYAAACCUxUsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERER6ZeUKBMREREREREREREREZF+6f8DfZ3tCsQVApMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x127405510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "fig, axes = plt.subplots(nrows = 1, ncols = 3, figsize = (30,9));\n",
    "fig.subplots_adjust(wspace=0.15, hspace=0) # Bringing subplots closer together. \n",
    "axes[0].plot(step_list, cost_list);\n",
    "axes[0].set_figure\n",
    "axes[0].set_title('Cost vs Steps', size = 20);\n",
    "axes[0].set_xlabel('Steps', size = 17);\n",
    "axes[0].set_ylabel('Cost', size = 17);\n",
    "axes[1].plot(step_list, V_accur_list);\n",
    "axes[1].set_title('Validation Accuracy vs Steps', size = 20);\n",
    "axes[1].set_xlabel('Steps', size = 17);\n",
    "axes[1].set_ylabel('Validation Accuracy', size = 17);\n",
    "\n",
    "# predictions\n",
    "predicted = [np.argmax(predictions[i,:], 0) for i in xrange(0,predictions.shape[0])]\n",
    "#actual\n",
    "t_labels = [np.where(testing_labels[i] == 1)[0][0] for i in xrange(0,predictions.shape[0])]\n",
    "\n",
    "cm = metrics.confusion_matrix(t_labels, predicted)\n",
    "cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
    "confusion_matrix = pd.DataFrame(data = cm_normalized)\n",
    "sns.heatmap(confusion_matrix, annot=True, fmt=\".3f\", linewidths=.5, square = True,ax = axes[2], cmap = 'Blues_r', cbar = False);\n",
    "axes[2].set_ylabel('Actual label', size = 17);\n",
    "axes[2].set_xlabel('Predicted label', size = 17);\n",
    "confusion_title = 'Confusion Matrix ({}%)'.format(test_accuracy)\n",
    "axes[2].set_title(confusion_title, size = 20);\n",
    "fig.suptitle('Multinomial Logistic Regression', y=1,x = .52, size = 40);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is supposed to be the weights of the three\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAECCAYAAAAVT9lQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWcOF9tJbygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14e10a650>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "zero_visual = weights_matrix[:,3].reshape(28,28)\n",
    "confusion_matrix = pd.DataFrame(data = zero_visual)\n",
    "sns.heatmap(confusion_matrix, linewidths=.5, square = True, cmap = 'Blues_r', cbar = False);\n",
    "print('This is supposed to be the weights of the three')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 align=\"left\"> 1 Layer Neural Network </h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "Problem\n",
    "-------\n",
    "\n",
    "Turn the logistic regression example with SGD into a 1-hidden layer neural network with rectified linear units [nn.relu()](https://www.tensorflow.org/versions/r0.7/api_docs/python/nn.html#relu) and 1024 hidden nodes. This model should improve your validation / test accuracy.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "V_accur_list, step_list, cost_list = [], [], []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Setting up the Computation graph always comes first\n",
    "\n",
    "batch_size = 128\n",
    "n_hidden_nodes = 1024\n",
    "image_size = 28\n",
    "\n",
    "graph = tf.Graph()\n",
    "with graph.as_default():\n",
    "    \n",
    "    # Input data. For the training data, we use a placeholder that will be \n",
    "    # fed at runtime with a training minibatch\n",
    "    tf_train_dataset = tf.placeholder(tf.float32,\n",
    "                                    shape=(batch_size, image_size * image_size))\n",
    "    tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))\n",
    "    tf_valid_dataset = tf.constant(validation_data)\n",
    "    tf_test_dataset = tf.constant(testing_data)\n",
    "    \n",
    "    # Variables.\n",
    "    weights_01 = tf.Variable(\n",
    "    tf.truncated_normal([image_size * image_size, n_hidden_nodes]))\n",
    "    weights_12 = tf.Variable(tf.truncated_normal([n_hidden_nodes, num_labels]))\n",
    "    biases_01 = tf.Variable(tf.zeros([n_hidden_nodes]))\n",
    "    biases_12 = tf.Variable(tf.zeros([num_labels]))\n",
    "\n",
    "    # Training computation.\n",
    "    z_01= tf.matmul(tf_train_dataset, weights_01) + biases_01\n",
    "    h1 = tf.nn.relu(z_01)\n",
    "    z_12 = tf.matmul(h1, weights_12) + biases_12\n",
    "    loss = tf.reduce_mean(\n",
    "    tf.nn.softmax_cross_entropy_with_logits(z_12, tf_train_labels))\n",
    "\n",
    "    # Optimizer.\n",
    "    optimizer = tf.train.GradientDescentOptimizer(0.05).minimize(loss)\n",
    "\n",
    "    # Predictions for the training, validation, and test data.\n",
    "    train_prediction = tf.nn.softmax(z_12)\n",
    "    valid_prediction = tf.nn.softmax(\n",
    "    tf.matmul(tf.nn.relu(tf.matmul(tf_valid_dataset, weights_01) + biases_01), weights_12) + biases_12)\n",
    "    test_prediction = tf.nn.softmax(tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset, weights_01) + biases_01), weights_12) + biases_12) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized\n",
      "Minibatch loss at step 0: 71497.054688\n",
      "Minibatch accuracy: 7.8%\n",
      "Validation accuracy: 10.0%\n",
      "Minibatch loss at step 100: 1201.343384\n",
      "Minibatch accuracy: 13.3%\n",
      "Validation accuracy: 11.7%\n",
      "Minibatch loss at step 200: 2.302629\n",
      "Minibatch accuracy: 10.2%\n",
      "Validation accuracy: 11.4%\n",
      "Minibatch loss at step 300: 2.264886\n",
      "Minibatch accuracy: 19.5%\n",
      "Validation accuracy: 10.9%\n",
      "Minibatch loss at step 400: 2.303076\n",
      "Minibatch accuracy: 7.0%\n",
      "Validation accuracy: 10.9%\n",
      "Minibatch loss at step 500: 2.297338\n",
      "Minibatch accuracy: 12.5%\n",
      "Validation accuracy: 11.5%\n",
      "Minibatch loss at step 600: 5.509095\n",
      "Minibatch accuracy: 17.2%\n",
      "Validation accuracy: 10.9%\n",
      "Minibatch loss at step 700: 2.305910\n",
      "Minibatch accuracy: 8.6%\n",
      "Validation accuracy: 10.9%\n",
      "Minibatch loss at step 800: 2.301982\n",
      "Minibatch accuracy: 11.7%\n",
      "Validation accuracy: 10.9%\n",
      "Minibatch loss at step 900: 2.286079\n",
      "Minibatch accuracy: 10.2%\n",
      "Validation accuracy: 10.9%\n",
      "Test accuracy: 11.1%\n"
     ]
    }
   ],
   "source": [
    "num_steps = 1000\n",
    "\n",
    "with tf.Session(graph=graph) as session:\n",
    "    tf.initialize_all_variables().run()\n",
    "    print(\"Initialized\")\n",
    "    for step in range(num_steps):\n",
    "        # Pick an offset within the training data, which has been randomized.\n",
    "        # Note: we could use better randomization across epochs.\n",
    "        offset = (step * batch_size) % (y_train.shape[0] - batch_size)\n",
    "        # Generate a minibatch.\n",
    "        batch_data = x_train[offset:(offset + batch_size), :]\n",
    "        batch_labels = y_train[offset:(offset + batch_size), :]\n",
    "        # Prepare a dictionary telling the session where to feed the minibatch.\n",
    "        # The key of the dictionary is the placeholder node of the graph to be fed,\n",
    "        # and the value is the numpy array to feed to it.\n",
    "        feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels}\n",
    "        _, l, predictions = session.run(\n",
    "          [optimizer, loss, train_prediction], feed_dict=feed_dict)\n",
    "        if (step % 100 == 0):\n",
    "            cost_list.append(l)\n",
    "            step_list.append(step)\n",
    "            print(\"Minibatch loss at step %d: %f\" % (step, l))\n",
    "            print(\"Minibatch accuracy: %.1f%%\" % accuracy(predictions, batch_labels))\n",
    "            valid_accuracy = accuracy(valid_prediction.eval(), validation_labels)\n",
    "            V_accur_list.append(valid_accuracy)\n",
    "            print(\"Validation accuracy: %.1f%%\" % valid_accuracy)\n",
    "    test_accuracy = accuracy(test_prediction.eval(), testing_labels)\n",
    "    print('Test accuracy: %.1f%%' % test_accuracy)\n",
    "    #This line below allows for taking the predictions to a pandas dataframe and eventually a heatmap/confusion matrix\n",
    "    pred = test_prediction.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0., ...,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  1., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0., ...,  0.,  0.,  0.],\n",
       "       ..., \n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testing_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "colab": {
   "default_view": {},
   "name": "2_fullyconnected.ipynb",
   "provenance": [],
   "version": "0.3.2",
   "views": {}
  },
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
